Few-Shot LLM Extraction: Why It Works When Regex and NER Fail

Traditional data extraction methods like regex and NER models break down when confronted with the variability inherent in production data. This article demonstrates how few-shot LLM extraction using frameworks like langchain and models such as GPT-3.5-turbo achieves 95% accuracy while eliminating the need for constant retraining and complex pattern engineering. For organizations struggling with unstructured documents, this approach offers a pragmatic path toward maintainability, scalability, and cost-efficiency.